AI-Supported Adaptive History-Taking for Telemedicine

India faces major healthcare access challenges, especially in rural areas with limited doctors and long travel times. Telemedicine bridges this gap, but current clinical history-taking at the community level is inefficient—CHWs often collect lengthy, unfocused histories before connecting with hub doctors. Our project leverages AI and Large Language Models (LLMs) to create an adaptive, efficient history-taking tool that guides CHWs to ask relevant questions based on patient responses. This system reduces consultation time, improves information quality, and helps doctors make quicker, more accurate decisions. We are developing and optimizing this tool using agent-based simulations and prompt engineering, and will evaluate its performance with both expert clinical reviewers and in a real-world pilot in rural Nashik, India. This innovation aims to enhance care quality, reduce delays, and ultimately scale across India’s public telemedicine ecosystem, including eSanjeevani.